DBMS > Elasticsearch vs. mSQL vs. OushuDB vs. SAP HANA vs. Solr

System Properties Comparison Elasticsearch vs. mSQL vs. OushuDB vs. SAP HANA vs. Solr

Editorial information provided by DB-Engines

Name

Description

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

mSQL (Mini SQL) is a simple and lightweight RDBMS

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

In-memory, column based data store. Available as appliance or cloud service

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Search engine

Relational DBMS

Search engine

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	135.35
Rank	#7	Overall
	#1	Search engines

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	0.04
Rank	#363	Overall
	#154	Relational DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

www.elastic.co/elasticsearch

hughestech.com.au/products/msql

www.oushu.com/product/oushuDB

www.sap.com/products/hana.html

solr.apache.org

Technical documentation

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

www.oushu.com/documentation

help.sap.com/hana

solr.apache.org/resources.html

Developer

Elastic

Hughes Technologies

Oushu

SAP

Apache Software Foundation

Initial release

2010

1994

2010

2006

Current release

8.6, January 2023

4.4, October 2021

4.0.1, August 2020

2.0 SPS07 (April 4, 2023), April 2023

9.6.0, April 2024

License

Commercial or Open Source

Open Source

Elastic License

commercial

free licenses can be provided

commercial

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Server operating systems

All OS with a Java VM

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux

Appliance or cloud-service

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

schema-free

Flexible type definitions. Once a type is defined, it is persistent

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL-like query language

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

Full-featured ANSI SQL support

yes

Solr Parallel SQL Interface

APIs and other access methods

Java API
RESTful HTTP/JSON API

JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

C
C++
Delphi
Java
Perl
PHP
Tcl

C
C++

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

SQLScript, R

Java plugins

Triggers

yes

by using the 'percolation' feature

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

Hadoop integration

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

Memcached and Redis integration

yes

User concepts

Access control

Kerberos, SSL and role based access

yes

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